CARDIAC SIGNAL PROCESSING WITH ALGORITHMS USING VARIABLE RESOLUTION
DOI:
https://doi.org/10.15588/1607-3274-2025-3-14Keywords:
cardiac signal, segmentation, cardiac cycles, T-wave end, variable resolutionAbstract
Context. The proposed paper relates to the field of cardiac signal processing, in particular, to the segmentation of the cardiac signal into cardiac cycles, as well as one of the most important features definition used in cardiac diagnosis, the T-wave end.
Objective. The purpose and object of study is to develop an algorithm for processing the cardiac signal in the presence of interference that allows the identification of features necessary for diagnosis and, at the same time, does not distort the original signal as is usually the case when it is processed by band-pass digital filters to exclude interference, which leads to the original signal distortion and, possibly, loss of diagnostic features.
The proposed Method involves representing the cardiac signal as part of some image contour. Cardiac signal processing consists first of all in segmentation into cardiac cycles. Usually, R-waves are used to segment the cardiac signal into cardiac cycles, i.e., the sequence of R-waves in the processed part of the cardiac signal is determined. When determining the R-wave, a model is used that assumes an increase in the signal followed by a decrease, and the increase (decrease) rate must be greater in absolute value than a certain predetermined value. For a selected segment of the cardiac signal, the sequence of R-waves is determined at different resolutions. The answer is the sequence that is repeated for the largest number of resolutions and that is used to segment the cardiac signal into cardiac cycles. The T-wave model can be represented as a sequence of curved arcs without breaks. In one of the common cases, the T-wave is determined by the largest maximum of the cardiac signal within the cardiac cycle, following the R-wave. The end of the T-wave is determined by the first minimum following the already determined maximum for the T-wave. As in the case of cardiac signal segmentation, the maximum of the T-wave and the T-wave end are determined at different resolutions, and the answer is considered to be those values that coincide at the largest number of used resolutions.
Results. Algorithms for cardiac signal processing using variable resolution have been developed and experimentally verified,
namely, the algorithm for segmentation of the cardiac signal into cardiac cycles and the algorithm for T-wave end detection, which is of great importance in cardiac diagnostics. Means of cardiac signal processing, using the proposed algorithms, do not change the processed cardiac signal, unlike traditional means that use filtering of the cardiac signal, distorting the cardiac signal itself, which leads to distortion of the processing result.
Conclusions. Scientific novelty consists in the fact that algorithms of cardiac signal processing in the presence of interference using variable resolution typical of visual perception are proposed. The practical significance consists in the fact that the means of cardiac signal processing, using the proposed algorithms, do not change the processed cardiac signal, unlike traditional means that use filtering of the cardiac signal, distorting the cardiac signal itself, which leads to distortion of the processing result. The use of the presented tools in practical medical practice will lead to an improvement in the quality of cardiac diagnostics and, as a result, the quality of treatment
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